Mobile Experience Sampling Method: Capturing the Daily Life of Elders

Abstract

The aging of populations worldwide has emerged as an important focus of research and policy. Concomitantly, capturing the daily life of elders is becoming a major task for researchers and service providers. In a mobile internet environment, traditional methods are not adequate to support contextualized information behavior research on the elderly. Based on a comparison of six methods from four perspectives (context, time, user, and data), this paper introduces the mobile experience sampling method (mESM) as an effective approach to the study of elders’ everyday information behaviors. An overview of mESM is presented, and a general three-stage framework is proposed to discuss its implementation. We also offer suggestions to improve the efficacy of mESM in addressing the real conditions and characteristics of the elderly and discuss the method’s advantages, disadvantages and related problems from the perspectives of researchers, elders, and policymakers. Overall, we find mESM to be an ideal longitudinal method for capturing the contextualized day-to-day information behavior of elders.

Full text

Mobile Experience Sampling Method:
Capturing the Daily Life of Elders
Rong Hu
1
, Xiaozhao Deng
1
, Xiaoning Sun
2
, Yuxiang (Chris) Zhao
3
,
and Qinghua Zhu
4(&)
1
Southwest University, Chongqing 400715, China
2
Shanxi University of Finance and Economics, Taiyuan 030006, China
3
Nanjing University of Science and Technology, Nanjing 210094, China
4
Nanjing University, Nanjing 210023, China
qhzhu@nju.edu.cn
Abstract. The aging of populations worldwide has emerged as an important
focus of research and policy. Concomitantly, capturing the daily life of elders is
becoming a major task for researchers and service providers. In a mobile internet
environment, traditional methods are not adequate to support contextualized
information behavior research on the elderly. Based on a comparison of six
methods from four perspectives (context, time, user, and data), this paper
introduces the mobile experience sampling method (mESM) as an effective
approach to the study of elders’everyday information behaviors. An overview
of mESM is presented, and a general three-stage framework is proposed to
discuss its implementation. We also offer suggestions to improve the efficacy of
mESM in addressing the real conditions and characteristics of the elderly and
discuss the method’s advantages, disadvantages and related problems from the
perspectives of researchers, elders, and policymakers. Overall, we find mESM to
be an ideal longitudinal method for capturing the contextualized day-to-day
information behavior of elders.
Keywords: Mobile experience sampling method Daily life Elders 
Longitudinal research Real situation
1 Introduction
The world population is aging rapidly. It is estimated that by 2050, the proportion of
the global population aged 65 and over will reach 20% [1]. This demographic shift has
emerged as an important focus of both research and policy planning worldwide. Within
the field of information behavior research, capturing the daily life of older people has
become an overarching task for researchers and service providers, who hope to
understand the needs of older people and thus provide more effective information
services for them. However, in the emerging mobile internet environment, elders’
information behaviors are highly situational, and traditional methods are thus some-
times inadequate to capture the day-to-day information behaviors of elders. This study
aims to introduce a new approach—the mobile experience sampling method (mESM)
©Springer Nature Switzerland AG 2019
J. Zhou and G. Salvendy (Eds.): HCII 2019, LNCS 11592, pp. 46–55, 2019.
https://doi.org/10.1007/978-3-030-22012-9_4

—with which to investigate elders’everyday information behavior. Two principal
questions guide our research:
1. Why is mESM suitable for capturing the daily life of the elderly in the emerging
mobile internet environment?
2. What are the necessary steps to implement the mESM approach? How can mESM
be employed to effectively explore elders’information behaviors?
2 Why mESM?
Traditional methods for capturing data from the elderly include interviews [2–8],
surveys [9–11], experiments [12], diary-keeping [13], and general ESM [14,15]. In
addition, sensor-based method can provide real-time monitoring of older people [16–
18]. Together, these methods have played an important role in collecting qualitative or
quantitative data from elders. However, in the current mobile internet environment,
elders’information behaviors are always rooted in specific contexts. Accordingly,
when we try to go deep into the everyday life of the elderly, we need to capture not
only the qualitative or quantitative data of needs, behaviors, experiences and emotions
in a given time and place, but also the corresponding real-life situations in which data
are generated. Meanwhile, if a method can easily support repeated measurements of
daily life and build cumulative data sets for comprehensive and fine-grained analysis, it
will help us more accurately understand the rhythm and regularity of elders’day-to-day
information behavior. These research requirements prompt us to seek a more suitable
longitudinal method of capturing intensive information from the real-life situations
faced by the elderly.
The criteria for selecting such a method can, we suggest, be viewed from four
perspectives: context, time, user and data. Such an analysis suggests that traditional
methods may not be adequate for current daily-life research. The pertinent issues are
summarized in Table 1. First, in terms of ecological validity, interview, survey,
experiment and diary methods each face great limitations in collecting real-situational
data. Researchers using these methods obtain only fuzzy recall data, not a real-time
sample. Although a diary may help the respondents recall incidents and situations, it
can hardly capture the real situation in the moment. The general experience sampling
method (ESM) is designed to facilitate data collection concerning both the context and
content of individuals’daily life [18]. The sensor-based method likewise derives
greater ecological validity from its provision of context-sensitive raw sensor data in real
time.
From the perspective of time, interviews, surveys and experiments usually collect
transverse data at a specific point of time. They are implemented only once and are
typically classified as one-shot evaluation methods. Diary, general ESM, and sensor-
based methods, in contrast, permit repeated measurements of variables and collect data
cumulatively; they can be grouped as intensive longitudinal methods [19,20]. With
respect to the user’s participation and perception, most of these methods (interview,
survey, experiment, diary and general ESM) require active participation or self-
reporting, and the whole process is made explicit to the users. An exception is the
Mobile Experience Sampling Method: Capturing the Daily Life of Elders 47

sensor-based method, which collects data directly without user ’s participation and can
thus be characterized as implicit and passive, reducing the interruptions experienced by
users.
Table 1also presents five aspects of data as they apply to each method: data
characteristics, data size, the collection of emotional or experiential data, the data’s
semantic richness, and the presence of retrospective bias. In general, data collected via
interviews and diaries will be qualitative, whereas surveys, experiments, and sensor-
based methods usually collect quantitative data. Notably, general ESM can capture
both [18,21]. In terms of data size, surveys usually allow for a large sample, whereas
interviews, experiments, diaries, and general ESM are often restricted to a small sample
size; the sampling size of sensor-based methods can be large or small. Diary, general
ESM and sensor-based methods can collect cumulative data, while the other methods
obtain one-shot data. Sensor-based methods yield raw sensor data without semantics,
which gives rise to a problem of interpretation. Such methods, unlike the others, cannot
supply information about individuals’experiences and emotions per se. Since sensor-
based methods and general ESM can capture real-time data, these two methods have a
smaller retrospective bias.
The above comparison shows that traditional methods, such as interviews, surveys,
experiments and diary-keeping, cannot effectively capture real-situation data or facil-
itate longitudinal research. Although sensor-based methods can be applied to large or
small samples with implicit data collection, the data obtained by this method is only
raw sensor data, lacking semantic information. General ESM provides a good
methodological framework for studying daily life, helping to capture real situations and
supplying intensive longitudinal data; it can collect both qualitative and quantitative
data and supply semantically rich descriptions of experiences and emotions, but it is
complicated and inconvenient to implement (a point developed further below), espe-
cially when being used to study the elderly, and a small sample size is typical. Thus, in
a mobile internet environment, it is necessary to improve general ESM to allow for the
effective and convenient study of elders’day-to-day information behaviors.
Information and communication technology (ICT) offers tremendous opportunities
for both researchers and the elderly. As mobile technology gradually integrates into our
lives, a mobile phone has become a necessity, not a luxury. Increasingly, older adults
use mobile phones or smartphones to satisfy their everyday health, social, and leisure
needs. The corresponding information behaviors have been of great interest to
researchers. Meanwhile, more and more researchers have adopted mobile technology to
facilitate their elderly-related studies. In this paper, mobile experience sampling method
(mESM) is proposed as highly suitable for research on the day-to-day information
behavior of the elderly within this emerging mobile internet environment. mESM is a
longitudinal method that uses mobile technology to study behaviors and experiences
occurring naturally in people’s everyday life. It is, in essence, an experience sampling
method that inherits the implementation framework of ESM and improves upon it with
mobile technology. Herein, we aim to introduce mESM and its implementation
framework, and to contemplate potential improvements to mESM for studying the
daily life of the elderly.
48 R. Hu et al.

3 How to Use mESM
3.1 Make Good Use of the Implementation Framework
mESM is a descendant of the experience sampling method (ESM), a systematic phe-
nomenology approach proposed at the University of Chicago in the 1970s [18].
Typically, general ESM uses a tool to signal participants, allow them to answer
questions at random moments every day or complete a report following a particular
event of interest, achieving the purpose of data collection. It is essentially a self-report
method. Because participants voluntarily and spontaneously perform their reports in a
real and natural situation, ESM is ecologically valid. Through repeated measurement,
ESM can help to explore people’s dynamic and complex behaviors, experiences and
emotions.
Generally, the signaling tool and experience sampling form (ESF) are the two
important components of ESM [18], as shown in Fig. 1. Early ESM studies used a
setup known as paper-based ESM (ESMp), with pagers for signaling and paper ESFs
for data collection. After receiving a signal, ESMp participants filled out the paper ESF
immediately and mailed it back to the researcher as soon as possible (e.g. at the end of
the day) [22]. It was understandably difficult for ESMp researchers to control this
cumbersome process, and participants may have felt inconvenienced as well. Com-
puterized ESM (ESMc) was welcomed by researchers because it alleviated some of
these problems, allowed researchers to better understand the process of participants’
completion of the forms, and reduced the cost of data transcription. The ESM programs
ESP and iESP, for example—both developed by Intel Research [23]—used a PDA to
signal participants and collect data. However, researchers still needed to download and
aggregate data from every participant’s PDA after finishing their research. This created
Table 1. Comparison of six data capture methods
Interview Survey Experiment Diary General
ESM
Sensor-based
method
Context Ecological validity Low Low Low Low High High
Time Transverse ✓✓✓
Longitudinal ✓✓ ✓
User Participation Active ✓✓✓ ✓✓
Passive ✓
Perception Implicit ✓
Explicit ✓✓✓ ✓✓
Data Characteristics Qualitative ✓✓✓
Quantitative ✓✓ ✓ ✓
Size Sampling Small Large Small Small Small Large or
small
Cumulative ✓✓ ✓
Emotional or experiential ✓✓✓ ✓✓
Semantic richness High High
or low
High or
low
High High Low
Retrospective bias Large Large N/A Large Small Small
Mobile Experience Sampling Method: Capturing the Daily Life of Elders 49

problems with data synchronization and prevented ESMc from attaining popularity as a
tool for large-scale field research. The development of mobile devices, the proliferation
of wireless networks, and the growing popularity of online surveys led to the creation
of mESM, which highlights the advantages of using mobile technology. Modern
mESM software usually runs on smartphones, supports both signaling and ESF
completion, and has servers to support real-time synchronization of data. Some mESM
tools can even support context awareness and signaling based on sensors (e.g. GPS
sensors). Therefore, mESM greatly improves the convenience of everyday-life research
and makes it possible to enlarge the sample size. In addition, a mESM tool with sensors
may collect both explicit self-report data and implicit sensor data, thereby obtaining
more richly contextualized data and semantics. In short, mESM is an ideal method for
everyday-life research.
Table 2shows a detailed implementation framework for mESM. It can be divided
into three stages: before implementation (BI), during implementation (DI) and after
implementation (AI). In the BI stage, researchers need to select a sampling method,
determine a timeframe, choose an mESM tool, and design the ESF. Next, they must
recruit, select, and orient participants. Within ESM, there are generally three classes of
sampling method from which to choose (Table 2). In time-contingent sampling, par-
ticipants are signaled at random times or at different time intervals every day [19]. For
example, researchers may send a certain number of signals randomly between 7:00 am
and 10:00 pm every day. The event-contingent sampling method solicits self-reports
following a specific event of interest [18] (e.g. an interaction in social media). Mixed
sampling usually combines time-contingent sampling with event-contingent sampling;
for example, researchers may signal readers to complete self-reports at specific times; at
the same time, the readers may complete their reports once they have finished reading
an e-book.
Fig. 1. Evolution of ESM tools
50 R. Hu et al.

The timeframe decision concerns how many days participants will be asked to
report (research cycle) and how many times per day they will be signaled to provide
these reports (daily sampling frequency). Together, these two criteria determine the
sampling schedule. Some guidance can also be obtained from researchers’long
experience with general ESM: studies shows that a seven-day cycle is likely to yield a
fairly representative sample of the various activities individuals engage in and to elicit
multiple responses from many of these activities [18]. The most common daily sam-
pling frequency is three times per day (e.g. in the morning, at noon and at night) [24].
Sampling for longer than seven days or more frequently than six times per day may
place an excessive burden on some participants [18,25].
Although there are some ready-made mESM-style tools (e.g. Ohmage,Open Data
Kit,Paco,LifeData,Ilumivu,MetricWire,Movisens,Expimetrics,Aware,ESM cap-
ture, and Piel Survey)[21], researchers must still decide between a ready-made tool and
a custom tool according to the needs of research. It is also necessary to design an ESF
that can be completed within five minutes or less to reduce the burden borne by
participants.
In principle, anyone who can read and operate a smartphone can participate in a
mESM study. It is essential, however, that individuals voluntarily participate in the
study and can guarantee their completion of the entire research process. Because of the
richness of the data, studies with as few as 5 or 10 participants can produce enough data
to be used reliably in simple statistical analysis [18]. Certainly, with the support pro-
vided by an mESM tool, a larger sample size is possible. However, before actually
going into the field, researchers should have an orientation meeting and implement a
pilot test. Orientation will provide instruction about the procedure and strengthen the
research alliance by providing further explanation of the study’s goals and answering
any questions.
In the DI stage, participants first receive SMS or other signals, then fill in and
submit ESF anytime and anywhere. Researchers should track the research every day to
find missing data and send reminders to corresponding participants. Incentivization
(whether material or nonmaterial) and retention of participants are necessary; to realize
the latter, it is beneficial to provide a thorough and honest explanation of the study and
establish a relationship of trust. In this stage, researchers are highly recommended to
write memos every day, because memos provide more extensive and in-depth data and
thinking for mESM research.
In the AI stage, a debriefing interview may help researchers get more extensive
information. For example, participants are often asked whether they felt that the period
of signaling represented a “normal week”in their lives and whether any specific
activities or situations caused them to fail to answer the signal. After data cleaning, the
process of data analysis includes both response-level and person-level analysis [18].
The former involves the raw data submitted after each individual signaling, while the
latter involves summarizing and analyzing the raw data for each individual. According
to the underlying purpose of the research, this analysis may be qualitative (e.g. case
analysis) or quantitative (e.g. ANOVA, ordinary least squares (OLS) or hierarchical
linear modeling (HLM)) [18].
Mobile Experience Sampling Method: Capturing the Daily Life of Elders 51

3.2 Improvements for the Elderly
The above implementation framework provides basic guidance for mESM field studies.
However, there are some specific improvements to consider in studying the day-to-day
life of elderly people (those who use smartphones). First, older participants may not be
comfortable reading text in small fonts, so picture, voice, and video channels may be a
good choice. For example, items in the ESF may be displayed as pictures or videos, and
participants may complete their report as a voice recording. Second, researchers should
consider allowing elderly respondents to capture their experiences by taking photos,
which can also assist in recollection after the fact [26]. Third, the cognitive load of the
elderly should be taken into account: it is recommended to use mESM tools with a
simple interface and a simple feature set. Fourth, it should be acknowledged that health
problems are prevalent among the elderly; a large amount of sensor data involving
position, movement, etc., can be integrated into health information behavior research
conducted on elderly subjects. Fortunately, all of these criteria can be satisfied with
smartphone-based mESM; accordingly, our team are developing a mESM tool tailored
Table 2. Implementation framework for mESM
Stage Contents Details
BI Determine sampling method •Time-contingent sampling
•Event-contingent sampling
•Mixed sampling
Determine framework of time •Research cycle
•Daily sampling frequency
•Signaling schedule
Decide on mESM tool •Choosing a ready-made or customized tool
Design ESF •Controlling items of ESF
Recruit, select and orient
participants
•Basic requirements for participants
•Prerequisites of participation
•Number of participants
•Orientation
DI Send signals •SMS or other signals
Participants fill in and submit
ESF
•Anytime, anywhere
Track the research •Anytime, anywhere
Reminder participants to fill in •Timing and frequency of reminders
Incentives and retention •Material or nonmaterial incentives
•Explain the study and establish relationship of
trust
Create memo •Provide extensive data
AI Interview •Debriefing interview
Process and analyze data •Data cleaning
•Response-level analysis
•Person-level analysis
Note: BI: before implementation; DI: during implementation; AF: after implementation
52 R. Hu et al.

to the elderly. In addition, the sampling method, timeframe, orientation, sampling
schedule, incentives, and retention practices should be tailored both to the age of the
participants and to the purpose of the research.
4 Discussion
From a researcher’s perspective, mESM has become an ideal method for capturing the
day-to-day information behaviors of the elderly. Compared with general ESM, mESM
is more convenient and can capture qualitative or quantitative data explicitly or
implicitly for a large or small sample size. In addition, mESM tools are readily com-
bined with other methods, such as ethnography or field experiments [21]. Therefore,
widespread adoption of mESM is expected in various fields, including clinical medi-
cine, healthcare and pharmaceutical research, mobile health management, mobile social
and mobile education. However, repeated signaling inevitably disturbs the elderly, and
the development or selection of a tool, combined with orientation and the provision of a
monetary incentive, will tend to increase the cost of this method. Additionally, if a
study integrates sensors, the investigators will face the challenges inherent in dealing
with heterogeneous data.
The perspective of the elderly, too, must be taken into account. Researchers should
favor reporting methods that are accessible, easy to navigate, and not cognitively
burdensome. Moreover, an effort must be made to improve the ICT literacy of the
elderly, and the privacy issues arising in an mESM-based study should be managed so
as to protect elders’rights.
Policymakers also have a role to play. Given the method’s potential value for
understanding the needs and challenges of the elderly, the government should
encourage mESM studies with elderly respondents. Official guidance for research and
related industries is also important, as are clear policies on mESM-related privacy
protection.
5 Conclusion
In sum, mESM is an ideal research method that combines the strengths of classic ESM
with current mobile technology. Although there are still some challenges in applying
the method to the day-to-day life of older people, mESM shows evident promise in this
field. With the support of mESM-based studies, we may understand the elderly more
accurately, facilitate older adults’self-management of daily life, choose policies that
better match the needs and characteristics of elderly citizens, and enable service pro-
viders to provide more accurate context-based services for this growing demographic.
Acknowledgements. The authors would like to thank the reviewers for their insightful com-
ments, which have improved the paper. This study has been supported by the Major Project of
National Social Science Foundation of P. R. China (Grant No. 15ZDB126), the Humanities &
Social Science Youth Foundation of Ministry of Education of P. R. China (Grant Nos.
16XJC870001, 18YJC870018), the Social Science Planning Foundation of Chongqing in
Mobile Experience Sampling Method: Capturing the Daily Life of Elders 53

P. R. China (Grant No. 2016PY76), the General Project of Philosophy & Social Science
Research in Colleges and Universities in Shanxi Province of P. R. China (Grant No. 201803021),
and the PhD Foundation of Southwest University in China (Grant No. swu118021).
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